Determining in-season crop status in an agricultural crop and alerting users

ABSTRACT

A crop&#39;s status may be determined from analysis of information regarding the crop received from, for example, a user, an in-season data gathering source, a database, a data feed, an aerial sensor, a UAV, and/or a remote sensor via a communication network. The received data may be compared to, for example, a benchmark, a parameter, a previously determined crop status, and/or a baseline associated with the crop and the comparison may be scored. When the score exceeds a predetermined threshold, an alert may be generated and provided to a user via the communication network.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.13/568,109 filed Aug. 6, 2012 for “METHODS, APPARATUS, AND SYSTEMS FORDETERMINING IN-SEASON CROP STATUS IN AN AGRICULTURAL CROP AND ALERTINGUSERS” by Jerome Dale Johnson.

INCORPORATION BY REFERENCE

The aforementioned U.S. application Ser. No. 13/568,109 is herebyincorporated by reference in its entirety.

BACKGROUND

The present invention relates to the methods, graphical user interfaces(GUI), computer-readable media, and systems for combining multiple typesof data and data sources including in-season crop data related to cropstatus in agricultural crops, dynamically analyzing the data, makingcrop status determinations based on that analysis and user definedtriggers, and automatically providing alerts to the user, or otherdesignated parties, concerning the status. The present invention detectsthe status and alerts the user in a timely manner during the crop seasonsuch that action can be taken. The system can be executed repeatedly andconsistently in a cost-effective, scalable manner and without requiringspecial agronomic or technical skills.

It is well known that reducing crop stress during the growing season isimportant to maximize crop quality, yield and economic return. As thecrop grows and matures it is subject to a variety of factors that cannegatively impact crop outcomes. The term crop stress as used in thepresent document to describe the crop stress resulting when the factorsthat cause crop stress during the crop life cycle and can be controlledor managed to some degree but are not managed as effectively aspossible. Examples of these factors include nutrient and pH imbalance,insect and other pests, diseases, and other conditions which impact acrop during the growing season. The present document will use nutrientimbalance and in some cases a specific nutrient such as nitrogen as anexample of a factor that can cause stress to describe the presentinvention. It should be noted that the methods and systems describedherein apply also to pests and diseases even though the algorithmdetails may vary. As we describe the invention it should be noted thattimeliness and ease of use are of particular importance. By timelinesswe are addressing early detection of crop stress, determining theseverity of the stress and responding to the stress quickly minimizingtheir negative impact. For the crop stress issues addressed in thepresent document corrective action needs to occur as quickly as possibleto minimize the negative impact on the crop and its quality and yield.

Matching the balance of nutrients available for a plant with thenutrient requirements of that plant, at any point in time during theplant life cycle, is critical to reduce stress and to maximizeagricultural output and value. Matching available/required nutrientbalance is especially important during key times in the plant's lifecycle. Nutrient levels below what is required may result in yield lossor a decline in crop quality, and therefore economic loss. Nutrientlevels above what is required can result in excessive costs and stressedplants, which is harmful for both plants and humans consuming them, andcan negatively impact surface and ground water and has other harmfulenvironmental effects.

While it is important to match the available nutrients to those requiredby each plant, this balance is difficult to achieve, especially in ascalable manner required in today's large-scale production agricultureoperations where a farmer may have thousands of acres located in fieldsdistributed over many miles. If a crop stress resulting from nutrientimbalance is identified shortly after it first begins to impact theplant and action is taken to add nutrients as soon as possible,permanent losses in yield, crop quality, and the resulting negativefinancial impact can be minimized.

The primary problem, which is addressed by the present invention, is theability to determine the crop stress as quickly as possible, in-season,and ideally before any, or at least minimal, damage to the crop hasoccurred, and then make the farmer (grower, farm manager, consultant,supplier, contractor, or other person with the responsibility to monitorcrop health, henceforth collectively identified as the user in thepresent document) aware of the stress such that corrective action can betaken immediately or soon thereafter. In addition, the present inventiondetects the stress and notifies the user in a scalable and cost effectmanner. The process can be executed repeatedly and consistently andwithout requiring special agronomic or technical skills.

Continuing to use nutrients as an example, laboratory soil testing iscurrently widely used to determine the level of nutrients in the soil,however, it does not always accurately estimate the nutrients currentlyavailable for the crop planted in that soil. These tests are oftenperformed many months before a crop is planted, in some cases yearsbefore, and may not reflect the nutrients available for a plant during aparticular growing season. Another drawback of soil nutrient tests isthat they do not take into account factors such as soil structure orbiological activity, nor to they take into account weather and otherfactors, which affect the rate at which nutrients leave the soil intothe surrounding environment. These, and other factors mean that theconcentration of nutrients in the soil can rapidly deviate from theresults of a soil test.

Plant tissue tests are almost always more accurate than soil tests whendetermining the condition of the plant and the need for additionalnutrients. They are more accurate because they include additionalinformation about the physiology of the crop and the actual status ofcrop nutrition rather than nutrients in the soil. Traditional tissuetests are destructive tests where a sample is sent to a laboratory foranalysis. Non-destructive tissue tests have advantages over traditionaldestructive tests in that they can be performed in the field, andprovide results much faster than laboratory tests, however these testsare subject to many of the same limitations and constraints astraditional laboratory tests.

The problems with laboratory tests (both soil and plant tissue) are thatthe results are often difficult to interpret. Also, there is a delaybetween when the samples are taken for analysis, and the delivery ofthat analysis to the farmer. This means the results may not be receivedby the farmer until the ideal time to take corrective action has passed.In addition, laboratory tests can be quite expensive, and they areusually performed randomly across a field and therefore can only, by thenature of sample testing, provide a loose estimate of the nutrientstatus over the entire field and may not reflect the variability foundthroughout the field and special problems in specific portions of thefield.

Another method currently used to determine crop status is manual visualinspection, commonly called “scouting”. This is usually accomplishedwhen the farmer or a contracted expert visually inspects the crop, byliterally walking through or driving by the field. This technique isineffective because the farmer can only inspect a small portion of thecrop, is random in nature, and requires ambition, skill andinterpretation in the person performing the scouting. This approach alsobecomes less practical as farm operation grows in scale.

Aerial visual inspection, using aircrafts, satellites, or other flyingdevices, is also used from time to time. This approach allows the farmerto scout a larger portion of the crop from an advantaged aerial positionin a short time. This approach also depends on the skill of the farmer(or pilot, if the aircraft is manned) to make visual interpretations ofthe data gathered. Depending on the method of aerial inspection, thisapproach can be costly and therefore cannot be reasonably conducted on aregular, such as daily, basis. Satellites, another source of data, cantypically capture imagery data infrequently, often every few weeks, andweather (e.g., clouds) can be an obstacle. The data is captured from avery high elevation making the resolution of the data problematic.Manned aircraft, satellite, or unmanned aerial vehicle (UAV) data ismost often in a visual form, and actionable interpretation is difficultunless relying on a person skilled in the art. As a result, satellitesand manned aircrafts cannot be depended on for timely crop statusdetection and are better used as a data source for long term planning.

Yet another approach to determine crop status is to use yield dataproduced by harvesting equipment, which is generated when the crop isharvested. Overlaying yield data, typically in the form of maps, fromseveral years illustrates yield and yield variability over time, and canbe useful when making long term plans. However, yield maps are notparticularly useful during the growing season when it is important todetect crop status as timely as possible.

Finally new methods and devices, such as an attached device to anutrient applicator or sprayer or other in-field device, may be used todetect a deficiency as the nutrient is being applied to the field orplant or an operation is performed in the field. When these machinestravel back and forth across a field they dynamically make adetermination of crop status and then apply the nutrients based on theanalysis. These machines are costly, require a pass over the field, andare helpful when applying supplemental nutrients variably, but not ashelpful when determining that there is a deficiency and when to takecorrective action. Currently, the farmer is left with using one or acombination of all of these techniques, resulting in data for him or herto work with that is generally difficult to interpret, delayed in itsusefulness, incomplete, costly, not scalable, and/or not science-based.

Different types and amounts of nutrient applications (such as manure orcommercial nutrients) provide an additional set of factors to consider.Nutrient levels available to a plant can vary across the field dependingon the chemical makeup of the nutrient, and how it reacts and isabsorbed by the plants and soils. Plus, proper application is a frequentproblem, caused by operator error, equipment malfunctions, and/orimproperly calibrated application equipment.

The present inventor has recognized that current in-season crop statusdetermination methods suffer from the same general problems, namely thelack of a repeatable, consistent, scalable, cost-effective,easy-to-interpret, and timely method to detect crop stress such asnutrient deficiencies in-season for today's production agricultureindustry. Methods today are difficult to use and each come with theirown set of technical, economic, and timing barriers. They do not takeadvantage of technologies such as timely, frequent, and cost-effectivein-season data-gathering, mining, federation, and analysis toconsistently and automatically make science-based determinations of cropstatus and crop stress such as nutrient status. Nor do currentstrategies adapt well to changes occurring in agriculture, namely, theeconomic need to maximize production, the increasingly largeragricultural operations, the increasingly more common use of unskilledworkers, and the continual need to accommodate the occurrence ofunplanned events such as inclement weather and climate change.

The present invention solves these problems by providing an analysis andalert system that can receive real time in-season crop data from UAVs(but not necessarily limited to that data source) dynamically combinethe received data with additional data, process and analyze it to makedeterminations and notify the user, or other designated parties, ofinstances where there is a crop status that varies from parametersdefined by the user. These parameters of measurement can be based on,for example, benchmarks established by the user him- or herself,benchmarks established by the present analysis and alert system,Internet-based research and other resources, and/or peer farmers and theresults they are achieving. These notifications of crop status to theuser or other designated parties can be provided on a timely basis suchthat corrective action can be taken consistently, repeatedly, andeconomically, and without agronomic or technical skill These objectivesare accomplished by employing technologies not previously exploited tosuch ends.

SUMMARY

The methods, apparatus, and systems for determining in-season cropstatus in agricultural crops and notifying designated parties of cropstress such that corrective actions can be taken are herein described.The crop status alerts system is comprised of a user interface, datafeeds, data sources, a communication network, a crop status analyzer andalert generator, and a database. Information regarding in-season cropstatus may be received from a variety of sources, such as a user, adatabase, a data feed, a social network, an Internet-based data source,a UAV, an in-field sensor, and/or equipment, via a communicationnetwork, such as the Internet, a cloud computing network, a local areanetwork (LAN), a wide area network (WAN), or a wireless LAN (WLAN).

The user interface may be configured to receive an alert, analysis, anddetermination from the crop status analyzer and alert generator via thecommunication network, provide the crop status analysis anddeterminations to the user, receive the information regarding the visualinformation, field data, planned events, and local knowledge from theuser, and provide the received information to the crop status analyzerand alert generator. Optionally, the system may further include adatabase communicatively coupled to the crop status analyzer and alertgenerator that is configured to store the received crop statusinformation.

The received information may be processed and analyzed to determine thestatus of a crop in a field and/or in a portion of a field. This statuscan be determined based on an analysis of the data including acomparison to, for example, previous data concerning the crop includingimages data such as patterns, color (visual and non-visual), texture,shape and shadows, non-visual data such as carbine dioxide levels,system-defined benchmarks, user-defined benchmarks, peer farmer-definedbenchmarks, other crops in similar environments, and/ornutrient/chlorophyll correlative data, to determine if stresses exist,if any. This analysis is intended to identify conditions that may impactcrop stress so that the user can take corrective action. The stressesthat are detected may encompass an entire agricultural field or aportion of the agricultural field. If the stresses are determined to bewithin a defined parameter of acceptability, no alert or notificationwill be issued to the user or other designated party. However, if theyfall outside the defined parameter of acceptability, the crop statusanalyzer and alert generator will issue the appropriate notification.

For the present invention, the user is generally assumed to be a farmeror other person who manages an agricultural crop. The aforementioneddesignated parties might include agricultural product and servicesuppliers, agricultural product buyers, landlords who rent land tofarmers, or other persons who have a vested interest and/orresponsibility in the growth and outcomes of an agricultural crop.

All of the data incorporated into the crop status alert system isderived from the user, the user's equipment, a UAV (or other flyingdevice, collectively identified as UAV in the present document),sensors, and/or commercial and/or public free and fee-based datasources. The graphical user interface (GUI) may be configured to receivedata from the user concerning the agricultural crop. This data mayrelate to the agricultural fields (location, size, shape, ID or name),planned events (dates, types, location, and other specifics of the cropplanted), and local knowledge (including, but not limited to, the user'spreferences and experiences, and his or her personal visual inspectionsof the crop). Other data may be received from other sources via acommunication network. This data incorporated into the crop statusanalyzer and alert generator may be from, for example, a UAV in the formof in-season data or from Internet-based data sources, relating to fielddata (soil types, weather patterns, climate, slope, etc.), unplannedevents (current weather data, etc.), and scientific and agronomic data(including, but not limited to, known best practices, research, plantresearch, extension, and universities). On some occasions, an attributeof the received information may be determined and the receivedinformation may be incorporated into a corresponding attribute of thedatabase. For example, when an attribute of the received informationrelates to the crop or field's condition, it may be incorporated into acorresponding field condition attribute.

A portion of the data that the user enters relates to his or herpreferences in how the crop status analyzer and alert generator receivesand analyzes the data, the parameters around how and when the systemnotifies the user or other designated parties, any exclusions that theuser desires exempt from the analyzed data, and the manner and method bywhich the user, and/or other designated parties, are alerted topotential stresses.

The crop status analyzer and alert generator sends alerts to the user,and/or other designated parties, through the communication network andthe GUI. In one embodiment, this notification may take the form of atext message, or a phone message. In another embodiment, thisnotification may include maps to define the location, size, and shape ofthe area where the stress that falls outside the user's establishedacceptable parameters has been determined. It may also include a visualanalysis in the form of a chart or graph displaying analysis,activities, and comparative data. The user to give the user a morenuanced view of the crop status data may alter data display preferences.In one embodiment, an example of a data display preference is theability of the user to exclude geographic areas within his or her fieldsthat he or she does not want included within the analysis. Thisexclusion allows the user to remove from consideration data and/or areasof a field that are physically incongruent with the rest of the field(e.g., ditches, rock piles, former building sites, special situationareas, etc.) and therefore skew or distort the overall dataset and theresulting analysis. Iterations of data gathering/receiving events mayoccur over a period of time, providing the user with comparative data ofthe same crop in the same field over time Likewise, through the use ofsocial networks, peer users may compare their crop health with others,including those other users who have crops in relative proximity andthose of other users who may share similar environments (soil types,climate, weather, seed varieties, etc.). For example, a when a farmerhas a disease or pest problem there is a high likelihood that otherfarmers in the social network may also have a similar problem. In thismanner crop stress findings and alerts can spread quickly across one ormultiple networks of farmers. In another embodiment, the user may beable to personally view the underlying data. Alerts may also be issuedto other interested parties, as designated by the user. These alerts areintended to keep the suppliers, buyers, landlords, and others abreast ofthe in-season crop growth progress.

In an exemplary method, data regarding a status of a crop may bereceived from, for example, a user, a contact of the user (e.g., anemployee of the user, a peer farmer, or a party linked to the user via asocial network), an in-season data gathering source, a database, a datafeed, an aerial sensor, a unmanned aerial vehicle (UAV), and/or a remotesensor via a communication network.

The received information may include one or more of following attributesof the crop and/or the land on which the crop is grown, nutrient level,a water level, an indication of pest infestation, an indication of pestdamage, an indication of unwanted vegetation infestation, an indicationof disease infestation, an indication of disease damage, local knowledgeof the crop, local knowledge of the land, crop characteristics, weatherdata, information regarding a planned event, information regarding anunplanned event, soil characteristics, geographic characteristics,geologic characteristics, and climate characteristics.

The received data may be evaluated and then scored. The scores may beautomatically compared to, for example, a benchmark, a parameter, apreviously determined crop status, and/or a baseline status associatedwith the crop.

When the score exceeds a predetermined threshold an alert may begenerated and provided to a user via the communication network. In oneembodiment, the predetermined threshold is user configurable. The alertmay indicate a level of crop distress and or a location of cropdistress. Exemplary alerts may include one or more of the score, avisual representation of a location of crops with scores that falloutside the predetermined threshold, a visual representation of a trendfor crop status over a period of time, a chart showing crop statustrends, a visual display of locations excluded from crop statusdeterminations, a visually enhanced display of certain aspects of theinformation as selected by the user. In some instances, the alert mayinclude information about the crop received at different times.

In some embodiments, the alert may be generated upon an occurrence of anevent, such as a weather event, a predicted yield of the crop (e.g.,when optimum crop ripeness is detected), detected level of water, cropdamage, or stress, and/or a detected presence of a pest, a pollutant.

The alert may be provided to the user in a format compatible with adisplay device (e.g., computer monitor, screen of mobile communicationdevice, etc.) via which the user views the provided alert. Exemplaryusers to which alerts may be provided include farmers, managers,landlords, buyers of the crop, and suppliers of supplies, goods, and/orservices utilized in the farming or harvesting of the crop. In oneembodiment, the alert is provided to a plurality of users. In someinstances the alert may be provided to the plurality of users via asocial networking service (e.g., Facebook™, Google+™, etc.).

On some occasions, the alert may be tailored or configured according to,for example, a user characteristic, a user preference, a type of alert,and an amount by which the score exceeds the predetermined threshold,and/or a type of score. Often times, following a data event the alert isprovided within a time period (e.g., within a few hours or days) thatenables the user to take a corrective action to improve the status ofthe crop. On some occasions, one or more of the received information,the comparison, the score, and the alert in a database may be stored ina database.

On some occasions, stored received information, and/or previouslygenerated comparisons, scores, and/or alerts may be accessed. Theadditional information may be compared with the accessed receivedinformation, the comparison, the score, and/or the alert and a change incrop status may be determined responsively to the comparison. In someembodiments, a trend in the crop status may further be determinedresponsively to the comparison.

In some embodiments, the benchmark, parameter, previously determinedcrop status, and/or baseline may be generated using, for example, thereceived data, comparison, and/or score. On some occasions, thecomparison and/or the score may be used to update, for example, thebenchmark, the parameter, the previously determined crop status, and/orthe baseline.

In one embodiment, instructions regarding how to process (e.g.,evaluate, score, and/or compare) the received information may bereceived from the user and the received information may be processedaccording to the received instructions.

In one embodiment, a recommendation based upon, for example, thecomparison and/or the score may be determined. The recommendation mayinclude, for example, one or more present or future actions the user maytake to address the subject matter of the alert, decrease crop stress,and/or or improve crop health.

In an alternate embodiment, information regarding a status of a crop maybe received from, for example, a user, an in-season data gatheringsource, a database, a data feed, an aerial sensor, an unmanned aerialvehicle (UAV), and/or a remote sensor via a communication network.Baseline data for the crop may then be automatically created using thereceived crop status information.

Additional information regarding a status of a crop may then be receivedfrom, for example, the user, the in-season data gathering source, thedatabase, the data feed, the aerial sensor, the UAV, and/or the remotesensor via the communication network. The additional received data maybe compared to the created baseline data and the comparison may bescored. When the score exceeds a predetermined threshold, an alert maybe generated and provided to a user via the communication network.

BRIEF DESCRIPTION OF THE DRAWINGS

The present application is illustrated by way of example, and notlimitation, in the figures of the accompanying drawings, in which:

FIG. 1 is a block diagram illustrating an exemplary system fordetermining crop status and providing an alert to a user, in accordancewith some embodiments of the present invention;

FIG. 2 is a block diagram illustrating an exemplary database, inaccordance with some embodiments of the present invention;

FIGS. 3 is a diagram illustrating an exemplary geographic informationsystem (GIS) data, in accordance with some embodiments of the presentinvention;

FIGS. 4A and 4B are flow charts depicting exemplary processes, inaccordance with some embodiments of the present invention;

FIGS. 5A-C depict an exemplary table of crop status data, in accordancewith some embodiments of the present invention; and

FIGS. 6-10 are screen shots of various user interfaces displayed to theuser that enable the user to interact with systems and system componentsdescribed herein, in accordance with some embodiments of the presentinvention.

Throughout the drawings, the same reference numerals and characters,unless otherwise stated, are used to denote like features, elements,components, or portions of the illustrated embodiments. Moreover, whilethe subject invention will now be described in detail with reference tothe drawings, the description is done in connection with theillustrative embodiments. It is intended that changes and modificationscan be made to the described embodiments without departing from the truescope and spirit of the subject invention as defined by the appendedclaims.

DETAILED DESCRIPTION

The present invention concerns methods and systems that combine,analyze, and process various types of data from various sources todetermine in-season crop status in plants and generate notificationsthat may be provided to and/or used by people engaged in productionagricultural operations. Crop status determinations and notifications,or alerts, generated in accordance with the present invention mayinclude reasons detailing the cause of said alerts. In some embodiments,a user may be able to manipulate various aspects of the definedparameters of status acceptability in order to ensure that he or shewill receive alerts when those alerts are the most effective and usefulfor that particular user and not become a nuisance, such as theproverbial boy crying wolf. The crop status analyzer and alert systemexecutes a process by which the crop condition and crop status data isreceived and analyzed, and the resulting determination of a status ofcrop status is reported to the user based on his or her pre-establishedparameters of acceptability.

In some cases, the crop status analyzer and alert system may be designedto include the user's local knowledge or requirements. For example, acrop status alert may be issued by the crop status analyzer and alertgenerator to the user based on inclusions or exclusions of data relatedto geographical locations or known crop or field conditions or practicesthat may be known only at the local level. If, in this example, the userdesires to exclude a portion of his or her field due to informationknown only at the local level, such as the presence of a former buildingsite or a manure, chemical or fertilizer spill in the past, that datamay, or may not, be incorporated into the analysis performed by the cropstatus analyzer and alert system. In this way, the excluded data doesnot adversely affect the accuracy of the received information orcalculations done thereon.

The present invention enables a user to identify problems with a crop'shealth, otherwise known as “crop distress” in the present document assoon as possible such that corrective action can be taken and thedistress rectified so that crop deterioration and yield loss isminimized. In the present embodiment, UAVs are the preferred method bywhich to gather data, however other sources, such as manned aircrafts,satellites, and remote sensors may also be used. The present documentwill not define controlling the manned or unmanned aircrafts Likewise,the present document will not define the type of images used. Thesetechnologies are well documented and while used by the crop statusanalyzer and alert generator, they are not the subjects of the presentinvention. This invention will focus on the aspects related todetermining crop status and alerting designated parties of such cropstatus.

Turning now to FIG. 1, a block diagram depicting an exemplary system 100for executing one or more of the processes described herein areillustrated. System 100 includes a communication network 105, whichcommunicatively couples a crop status analyzer and alert generator 110,a database 135, a user interface 125 (associated with a user 130), adata feed 115 (associated with commercial and/or public data source120), and an in-season data gatherer 140. Note, although only onecommunication network 105 is shown in the illustration, there may infact be multiple such networks and internetworks involved and suchnetworks and internetworks may be grouped together into communicationnetwork 105 for purposes of simplifying the present discussion. Further,in some instances some of the components illustrated in FIG. 1 may becombined or may be absent from instantiations of the present invention.For example, once the crop status alert has been generated, user 130 mayview the alert on personal computers, tablet computers, smart phones, orother portable computer-based devices, in which case the crop statusalert information may be self-contained and access to the communicationnetwork and other elements of system 100 may not be required until thecrop status alert or information concerning crop status needs to bemodified or updated. Although only one user interface 125 is shown,multiple such interfaces may exist. Thus, system 100 in FIG. 1 is bestregarded merely as an example of a system in which the present inventionfinds application.

As indicated, communication network 105 communicatively couples theother elements of system 100 to one another. Exemplary communicationnetworks 105 include cloud computing networks, the Internet, local areanetworks (LAN), wireless local area networks (WLAN), and wide areanetworks (WAN). Usually, though not necessarily, user 130 may connect tosystem 100 periodically to change his or her crop status monitoringpreferences (e.g., include or exclude certain geographic areas for thesystem's analysis, change the sensitivity parameters that the user haspre-established, or make other modifications). In some cases users 130may communicate crop status information to other users 130 such asemployees, consultants, buyers, suppliers, and landlords. In someembodiments, multiple users 130 may be enabled to communicate with oneanother via a communication network 105 in a manner similar to, forexample, a social network. The information exchanged via thesecommunications may be used to determine, for example, crop statusbaselines or benchmarks that extend beyond a singular operation. In someembodiments, crop status analyzer and alert generator 110 may reside ona computer-based platform, such as a server or set of servers. Such aserver may be a physical server or a virtual machine executing onanother hardware platform, however, the precise nature of such aconfiguration is not critical to the present invention.

Such a server, indeed all of the computer-based systems which arediscussed herein, will be generally characterized by one or moreprocessors and associated processing elements, interfaces, and storagedevices communicatively interconnected to one another by one or morebusses or other communication mechanism(s) for communicatinginformation. Storage within such devices will usually include a mainmemory, such as a random access memory (RAM) or other dynamic storagedevice, for storing information and instructions to be executed by theprocessor(s) and for storing temporary variables or other intermediateinformation during the use of the crop status alert system describedherein. Such a computer system may also include some form of read onlymemory (ROM) or other static storage device for storing staticinformation and instructions for the processor(s). A storage device,such as a hard disk or solid state memory may also be included forstoring information and instructions, such as the instructions tocompute crop status from externally gathered image data, and issuealerts if so required based on the pre-defined acceptability parameters.RAMs, ROMs, hard disks, solid state memories, and the like are allexamples of tangible computer readable media, which may be used to storethe instructions which comprise the methods for determining thenecessity of generating and presenting crop status alerts in accordancewith embodiments of the present invention. Execution of suchinstructions causes the various computer-based elements of system 100 toperform the processes described herein, although in some instances,hard-wired circuitry may be used in place of, or in combination with,such computer-readable instructions to implement the invention.

To facilitate user interaction, collection of information, and provisionof results, the computer systems described herein will typically includesome form of a display device, though such a display may not be includedwith the server, which typically communicates results to aclient/manager station (via an associated client/manager interface)rather than presenting same locally. Client/manager stations will alsotypically include one or more input devices such as keyboards and/ormice (or similar input devices) for communicating information andcommand selections to the local station(s) and/or server(s).

To facilitate the network communications alluded to above, the variouscomputer devices associated with system 100 typically include acommunication interface that provides a two-way data communication path.For example, such communication interfaces may be Ethernet or othermodems that provide a wired data communication connection or a wirelesscommunication interface for communication via one or more wirelesscommunication protocols. In any such implementation, the communicationinterface will send and receive electrical, electromagnetic, or opticalsignals that carry digital data streams representing various types ofinformation. This facilitates the exchange of data, including cropstatus analyzer and alert information, through network(s) 105 asdescribed herein.

Crop status analyzer and alert generator 110 may be configured togenerate a crop status alert by receiving input from user 130, data feed115, commercial and/or public data sources 120, in-season data gatheringsources 140, and/or accessing data stored in database 135. Crop statusanalyzer and alert generator 110 may use historical crop information inorder to, for example, determine a stage of development for a cropand/or determine crop status in a typical year.

Data feed 115 may provide remotely gathered data relating to, forexample, vegetation characteristics, weather (e.g., thunderstorms,tornados, temperature fluctuations), climate (e.g., average temperatureand/or rainfall), and geological data and events (e.g., mudslides,floods, earthquakes, etc.). Data feed 115 may be provided by, forexample, various public (e.g., the U.S. Department of Agriculture or theNational Oceanic and Atmospheric Administration) or private sources andmay be so provided on a fee or fee-free basis. Crop status analyzer andalert generator 110 may automatically include consideration ofhistorically known climate conditions (e.g., historic temperature orrainfall, etc.) for a geographic location when generating a crop statusalert. On some occasions, a data feed may be associated with a systemused or provided by an agricultural product supplier. On some occasions,data feed 115 may be provided by a social networking service (e.g.,Twitter, Facebook). In this way, one or more users may communicateinformation that may be relevant to, for example, crop status, statusupdates of current stress levels for peer farmers, or crop statustreatment methods and strategies of peer farmers between one another.Crop status alerts may be generated in a partially or wholly automatedmanner by crop status analyzer and alert generator 110 in response to,for example, analysis of peer group data, historical, real-time, and/orknown data relating to crop status.

Exemplary commercial and/or public data sources 120 include the Internet(public and private data services), subscription data sources. Combines,planters, sprayers and other equipment used to execute variousagricultural practices are another sources of data Other commercialand/or public data sources 120 may be extension, academic and/orresearch organizations, suppliers of crop inputs, buyers of crops, andpeer farmers.

In-season data gathering sources 140 may include UAVs, aircrafts,satellites, in-field physical sensors and/or equipment used to measurefield conditions for one or more fields or portions of fields includedwithin the crop status analyzer and alert system monitored area. Themeasurements are of the target field's crop condition including, but notlimited to, color (traditional and infrared), patterns, tone, texture,shape, shadow, temperature, size of the area, intuited nutrient levels,and/or information concerning the larger area in proximity to thetargeted field or portion of that field. For the sake of this document,UAVs are the primary data source for in-season crop condition data basedon their ability to gather data in a timely, quick, scalable, andeconomical fashion.

Database 135 may be one or a series of databases linked together and incommunication with crop status analyzer and alert generator 110.Database 135 may store data related to any facet of crop statusdetermination including, for example, current and historical data,including imagery produced by a UAV, satellite, or other aerial device.Database 135 may also include field location, soil characteristics,topography, historical weather, crop data, such as crop type, and seedvariety. Database 135 may further include crop characteristics andfarming practices, such as when and how the field is tilled and planted(for example, planted seed population), historical nutrientmeasurements, historical yield maps, notes, unplanned events, localknowledge, and planned events. Further details regarding the informationstored in database 135 are discussed below with regard to FIG. 2.

Generating a crop status alert can involve the user 130 manuallyselecting or entering, for example, various observations and preferences(e.g., areas to exclude, visually determined conditions, and/ornotification trigger parameters) for the area using the user interface125. A user may enter local knowledge into crop status analyzer andalert generator 110 for incorporation into the crop status analyzer andalert system. For example, a user may enter a period of time in which aparticular field will be analyzed, details concerning manureapplications, or observations made when planting or harvesting that maybe incorporated into the crop status analyzer and alert generator 110.On some occasions, manually selected preferences and other user-enteredinformation may be stored in database 135.

The crop status analyzer and alert generator 110 provides informationabout determined potential nutrient deficiencies to user 130. This maybe done in a variety of ways, including through the use of an e-mailand/or a message relayed via a messaging system accessible throughcommunication network 105 that includes hyperlinks to a portal at whichdetails regarding the crop status are available. Other forms ofcommunication, such as an instant message or a text message sent viashort message service (SMS) to a user's mobile phone may also be used toindicate a crop status alert trigger has occurred. In FIG. 1, userinterface 125 is meant to represent any device via which user 130 can beprovided with information regarding the crop status. Exemplaryinterfaces 125 include computer systems, mobile computing devices(including but not limited to so-called “smart phones”), tabletcomputing devices, and portable computing devices.

In some embodiments, one or more users 130 may be enabled to access acrop status analysis via user interface 125 communicatively coupled tonetwork 105. Interfaces for various types of users may be different inform and content, or similar to user interface 125. Exemplary users 125include employees, managers, owners, equipment operators, suppliers,consultants, regulators, and others who assist user 130 in thedetermining, and/or executing a corrective strategy.

FIG. 2 is a block diagram depicting exemplary sets of data or databasesthat may be included in database 135. For example, database 135 mayinclude field data 205, climate and weather data 210, local knowledgedata 215, geologic/geographic data 220, planned and executed event data225, supplier data 230, buyer data 235, landlord data 240, crop data245, and trigger and alert data 250. Information stored in database 135may be received from, for example, a user, such as user 130, a datafeed, such as data feed 115, an in-season data gathering source, such asin-season data gathering source 140, via a communication network, suchas communication network 105, and/or a combination of the foregoing.

Field data 205 may include information regarding, for example, fieldlocations, the shape of the field, the proximity of the field to otherrelevant locations such as other fields managed and farmed by the user.In this embodiment, field data may include field data for other farmers'fields. It may also include the field's characteristics, such astopographical information, soil types, organic matter, moisturecondition and capacity, fertility, and other non-crop vegetation on thefield. In addition, field data 205 may include historical cropproduction data on the field, including former crops planted andhistorical yields, including yield maps illustrating yield variabilityacross the field, as-planted maps, and tile maps. In addition field data205 may include historical fertility test results and practices specificto that field including for example, tillage and irrigation. On someoccasions, field data 205 may include areas of land proximate to thecrop to be excluded from analysis.

Climate and weather data 210 may include information relating tohistorical and predicted weather and/or climate conditions for aparticular region, area, or field.

Local knowledge data 215 may include information relating to knowledgeor preferences specific to a user and may include, for example,preferred agronomic and other crop production practices, site-specificknowledge, past experiences, activities, observations, and outcomes. Onsome occasions, local knowledge data 215 may be used to override ormodify an aspect of a crop status analysis. On some occasions, localknowledge data 215 may include data received via a social network fromother users.

Geographic/geologic data 220 may include geographic and/or geologic datarelated to, for example, fields, which are included in thedetermination, analysis, and alerts. Exemplary geographic or geologicdata may include roadway, surface and/or underground water, and landmarklocations. Geographic/geologic data 220 may be derived from a variety ofsources, such as satellite images, global positioning information,historical information regarding an area of land, plat book serviceproviders, non-governmental organizations, and public and privateorganizations and agencies.

Planned event data 225 may include information regarding planned eventsproceeding, during, and/or following completion of the crop-growingseason. Exemplary planned events may relate to activities such as whencrops are planted and the seed specifications and planting information,such as planted seed locations and population, scouting events (e.g., onsite crop inspections), out-sourced fertility tests, follow-upassessments, scheduled aerial data gathering events, and treatmentevents.

Supplier data 230 may include supplier information (e.g., names,locations, services, products, prices, contractual information, etc.),as well as delivery and/or instructions, dates and other specialactivities related to crop status analysis and alerts.

Buyer data 235 may include data that relates to obligations andspecifications that a buyer of an agricultural crop may have imposed onthe farmer that impact the crop status analysis and crop status, suchas, for example, restrictions, response requirements, standards,notifications, schedules, requirements, and the like.

Landlord/lender data 240 may include data that relates to obligationsand specifications that a landlord and/or lender may have imposed on thefarmer that impact the crop status, such as, for example, restrictions,response requirements, standards, notifications, schedules,requirements, and the like.

Crop data 245 may include crop conditions over the growing season asdetermined through various sensing methods, such as through UAVs orvisual observations, and through the user's local knowledge. It mayinclude previously performed analyses and determinations of crop status.

Trigger and alert data 250 may include specific measurement parametersthat, if exceeded, cause an alert to be triggered and sent to the user.In the present embodiment the triggers are preset to defaults by thecrop status analyzer and alert system. However, the user can overridethe default triggers on a field and/or operational level if he or shefeels the desire to do so. Additional data in this database may includehistorical determinations, and alerts that have been sent to the user.

On some occasions, the geographic and/or geologic data 220 may be partof a geographic information system (GIS), an example of which isillustrated in FIG. 3. As shown, GIS layers image 300 includes variousdata structures, each of which may be regarded as a layer. These layersprovide information regarding various data elements of a crop statusanalysis and alert for a field, including, for example, geographic data,field data, crop status analysis data, and crop status alert data.

Exemplary geographic data may include, for example, information relatedto an area of land (the field plus adjacent areas) (e.g., latitude,longitude, etc.), historical weather and climate information, soilattributes (e.g., soil types, texture, organic matter, fertility testresults, etc.), the presence and location of ground and surface water,and any man-made features upon the land (e.g., buildings, roads,ditches, etc.) currently existing or formerly in existence. Exemplaryfield and crop data may include the location, size, and shape of thefield, and/or may be related to tiling information. Exemplary localknowledge may include special insights concerning the field that onlythe farmer farming the field would know. It may also include commentsand data related to special events and visual observations. Historicalcrop and outcome data may include former crops planted and yields,fertility tests, and fertilizer applications. Exemplary crop statusanalysis data may include requirements imposed on the farmer by thelandlord, lender, or buyer of the crop and/or instructions and contractswith the supplier of crop inputs and services. Crop status analysis datamay also include data shared from other farmers, and establishedparameters, baselines, benchmarks, and scores. Crop status alerts datamay be those issued alerts that are stored in database 135.

FIG. 4A is a flow chart depicting an exemplary process 400 fordetermining a crop's nutrient status and providing and message or alertto a user responsively to the crop's determined nutrient status inaccordance with an embodiment of the present invention. Process 400 maybe executed by, for example, the crop status analyzer and alertgenerator 110 described in connection with FIG. 1 in cooperation with,for example, any of the systems and/or system components disclosedherein.

In step 405, data regarding a crop status may be received by, forexample, crop status analyzer and alert generator 110. The data may bedata produced by, for example, a UAV, an in-field sensor, commercialand/or public data sources, or data entered by a user based on a visualinspection. The data may be received from any number of sources,including a user, like user 130 (via a user interface, such as userinterface 125), commercial and/or public data source 120, a database,such as database 135, a data feed, such as data feed 115, and anin-season data gathering source, such as in-season data gathering source140. The data may be received via a communication network, such ascommunication network 105.

Exemplary received data may relate to any or all factors affecting acrop's nutrient status, such as, but not limited to, the crop's overallhealth, crop maturity, characteristics of land on which the crop isgrown (e.g., geographic location, size, soil characteristics, weatherand climate data, etc.), planned events (e.g., fertilization,harvesting, or irrigation schedules), unplanned events (e.g., severeweather events), local knowledge of the crop and/or the land on whichthe crop is grown (e.g., previous success rates with crop growthstrategies), historical patterns, scientific research, levels of pest,disease, and/or unwanted vegetation infestation, amounts of particularnutrients found in the crop and/or land on which the crop is grown,water levels found in the crop and/or land on which the crop is grown,and crop characteristics (e.g., maturity rates, genetics, growthcharacteristics, or disease resistance).

Next, the received data may be processed in order to, for example,reformat the data, remove duplicate data, organize the data, and/orconsolidate data (step 410). In some instances, the processing mayinclude dividing an image or information relating to a field upon whichthe crop is grown into sub-divisions or sections using, for example, aCartesian grid pattern, a concentric circle pattern, or a wedge-shapedpattern. The size of the sections may be a default value or determinedby the user for example the user may want the size of the section tomatch the size of the equipment to be used. The size of sections mayvary from acres to square feet, depending on, for example, the size ofthe field, the precision of the data source, the precision of the dataanalysis equipment, and/or user preferences. For example, when the cropis growing on a field with relatively large crop status variability, thesections may be relatively small in order to more precisely analyze thefield. In another example, when a general overview of crop status isdesired, the sections may be relatively larger in order to, for example,capture a general sense of crop status and minimize processing resourcesspent on unnecessary details.

It is understood that across a particular field, crop status may varydue to a number of factors such as nutrient application inconsistencies,variable manure nutrient value, soil variability, and the unevenness ofrainfall, to name a few of these factors. Dividing a field into sectionsenables analysis of each portion of the field independently of otherportions of the field. In some scenarios only one section may indicatecrop distress while all other sections indicate no crop distress (i.e.,normal crop condition). Localized crop distress may be an indicator ofwidespread crop distress to follow or the correction of conditions so asto alleviate crop distress.

In step 415, it may be determined whether parameters, crop statusdeterminations, and/or benchmarks are available. On some occasions,parameters, crop status determinations, or benchmarks may be indicativeof threshold values relating to a crop's status. Further discussion ofexemplary parameters, crop status determinations, and/or benchmarks isprovided below with regard to FIGS. 5A-C.

When parameters, crop status determinations, and/or benchmarks, are notavailable, one or more parameters, crop status determinations, and/orbenchmarks may be generated (step 420) using, for example, the datareceived in step 405 and/or data retrieved from, for example, a user,like user 130 (via a user interface, such as user interface 125),commercial and/or public data source 120, a database, such as database135, a data feed, such as data feed 115, and an in-season data gatheringsource, such as in-season data gathering source 140.

The data received in step 405 may then be compared with the parameters,crop status determinations, and/or benchmarks (step 425) and thecomparison may be scored (step 430). Exemplary scores may indicateoverall field nutrient status, nutrient status for a portion of a field,and/or changes in nutrient status for a field, or portion of a field,based previous crop status determinations.

In some embodiments, the score of step 430, indicated below as the newdata score, may be generated according to equation 1 provided below:

S ^(new)=(w(g ^(i))+w(g ^(s))+w(g ^(u))+w(g ^(lk))+w(g ^(om)))  [Equation 1]

wherein:

New Data Score=S^(new)

Weighting=w

Field Section=g

Field Section Image=g^(i)

Field Section Soils=g^(s)

Field Section Weather=g^(w)

Field Section Local Knowledge=g^(lk)

Field Section Organic Matter=g^(om)

Parameters=X← →Y

The comparison score (S^(compared)) may then be generated according toequation 2 provided below:

Compare S^(new) against→S^(benchmark)=S^(compared)   [Equation 2]

wherein S^(benchmark) represents the parameters, crop statusdeterminations, and/or benchmarks of step 425.

Further details regarding the comparison and scoring of steps 425 and430 are provided below with regard to FIGS. 5A-C.

It may then be determined whether the score exceeds a threshold amount(step 435). When a score does not exceed a threshold amount, process 400may end. When a score does exceed a threshold amount, an alertindicating the outlier score and/or one or more reasons for the outlierscore (e.g., level of crop stress, type of crop stress, trend in cropstatus, etc.) may be created (step 440) and provided to a user via, forexample, a communication method selected by the user (e.g., email, phonecall, SMS text message) (step 445).

In some embodiments, equations 3 and 4, provided below, may be used toexecute steps 435 and 440 of process 400.

If Scompared is>X parameter and/or<Y parameter, then create alert  [Equation 3]

If Scompared>X← →Y<Scompared, then create alert   [Equation 4]

Exemplary content of the alert message includes a reason for the alert,the date and condition of the last crop status data sample, thelocation(s) of the determined change in crop status, an area of landexcluded from analysis, and an area of land determined to have activatedthe trigger of an alert. Of course, the actual content of an alertmessage will depend on the embodiment and can differ for many reasonsincluding, for example, the preference of the user, type of crop, thetype of device the alert is sent to, or severity of the crop status.Another example of message content is a simple notification to “check afield” or maintain surveillance of a field on a “watch list” with littlespecificity as to the determination or severity of the crop status. Insome embodiments the content of the notification may differ based on therole of the individual user receiving the alert. For example, a suppliermay receive a message with information that differs from a messagereceived by a peer farmer included on the distribution list.

In some embodiments, the alert may be sent to multiple recipientsincluded in a distribution list defined by the user via a communicationmethod or combination of communication methods selected by the userand/or an individual recipient. The distribution list may includeindividuals or organizations that should know about the crop statusdetermination or those who may be helpful and could take action toquickly remedy the crop status. Examples of people who a user may wantto include in a distribution list are him- or herself, a farm manager,consultant, supplier, buyer, landlord, peer farmer, and/or banker. Insome embodiments the user may want to notify other peer farmers usingthese methods, however, the user may also use a type of social networkto provide notifications. Finally, the received data, the comparisondone thereon, the scores, and/or the created alerts may be storedwithin, for example, database 135 (step 450).

On some occasions, instructions or parameters for determining a crop'sstatus may be user configurable and these rules may be received by, forexample, the crop status analyzer and alert generator prior to theexecution of process 400. For example, instructions regarding theselection of a field or portions of a field on which the crop is grownfrom which data will be gathered may be received. Exemplary dataregarding the areas to be analyzed may include latitude and longitude,shapes, soils, slopes, topography, historical data, weather, crop,practices, and GIS data. The crop status analyzer and alert generatormay then use this data and combine it with other data available for thatarea. Instructions regarding what type of data to gather and thegranularity of the detail for the data may also be received from thecrop status analyzer and alert generator. Exemplary parameters mayinclude scores that indicate an unexpected change in normal andcustomary plant growth, an indication that the crop is deteriorating bya pre-defined measure, or a defined quantity of a field thatdemonstrates an indication of crop status.

FIG. 4B illustrates a process 401 for determining a trend in crop statusand providing and alert to a user responsively to the trend. Process 401may be executed by any of the systems and/or system components describedherein.

In step 455, new data regarding the status of a crop may be received by,for example, a crop status analyzer and alert generator, such as cropstatus analyzer and alert generator 110. Step 455 may be similar to step405 as discussed above with regard to FIG. 4A. The received data maythen be compared with historical data regarding the crop (step 460) andthe comparison may be scored in manner similar to, for example, step 430as discussed above with regard to FIG. 4A (step 465).

When the score does not exceed a threshold, the results of thecomparison and/or the score may be provided to the user and process 400may end. When the score does exceed a threshold, and alert may becreated (step 475) and provided to the user (step 480). Optionally, thealert created in step 475 may include the results of the comparison, thetrend, and/or the score of steps 460 and 465, respectively. Finally, thereceived data, the comparison done thereon, the trend, the scores,and/or the created alerts may be stored within, for example, database135 (step 485).

FIGS. 5A-C depict a table 500 of data regarding the status of a cropgrown on a particular field or section of a field. It should beappreciated that the data depicted in table 500 is merely exemplary andany single, combination, or sub-combination of crop status factors maybe considered when determining a crop's status or a trend in a crop'sstatus.

The crop status data of table 500 is divided into numerous categories orfactors 520. Exemplary factors 520 include crop stress, fertility added,soils, drainage, improvements, weather's impact on availability,management practices, production history, and planting. Each factor 520may have one or more sub-factors or categories 525 that may indicatemeasurements of the crop's nutrient status to any degree of granularity.For example, categories included under the crop stress factor includenear infrared (NIR) imagery, normalized difference vegetative (NDVI)index, chlorophyll indices, color imagery, and visual inspection.Categories associated with the fertility added factor 520 includefrequency of nitrogen application, timing of nitrogen application,starter fertilizer, manure, residue, and nitrogen as applied. Categoriesassociated with the soils factor 520 include texture, soil variability,tillage, organic matter, pH, and soil tilth (good soil physicalcondition with good aeration). Categories associated with the drainagefactor 520 include potholes, slope, and hills. Categories associatedwith the improvements factor 520 include tile and irrigation. Categoriesassociated with the weather's impact on availability factor 520 includeleeching of nitrogen, nitrogen followed totalization, and nitrogenprotection. Categories associated with the management practices factor520 include sampling type and management self-defined. Categoriesassociated with the production history factor 520 include yieldenvironment, crop rotation, yield consistency, and-of-season basal stalknitrate test. Categories associated with the planting factor 520 includetrait protected genetics.

Data regarding each of these may be received by, for example, a cropdata analysis and alert generator, such as crop status analyzer andalert generator 110. The received data may be processed with any degreeof granularity and a score 535 may be assigned using, for example, ascore matrix 530 that visually indicates the score of the crop, or cropstatus, with regard to a particular category. For example, the stressstatus of the crop as measured with NIR imagery has been assigned ascore of “6” as indicated on table 500 by the checkmark present withinthe minor stress status box. The score for a crop in a given categorymay also be indicated in the total score per category column 535 and theaggregated score for the entire factor may be indicated in the totalaggregated score column 540. The aggregated score may be calculatedusing one or more equations that incorporate the score and one or moreweights assigned to the factor (as shown in a factor weighting column510) and/or the category (as shown in a category weighting column 515and a). For example, the factor weighting assigned to the managementpractices factor is 2.1 and the category weightings for the samplingtype and management zone defined categories are both “3.” The individualscores for the sampling type and management zone defined categories are8 and 10, respectively. These scores, when multiplied by the factor andcategory weighting values and then added together guilt and aggregatescore of 113 for the management practices factor. Once calculated, theaggregated scores may be added together to generate a total score 555.

Information included with in the comments column 545 and additionalnotes column 550 may include information regarding, for example, afactor, category, crop characteristic, measurement data, equipment data,end and explanation for further details regarding a factor, a category,a score, and/or a weight. In other embodiment the notes may be reflectedas business rules integrated directly into the software.

FIGS. 6-10 illustrate various aspects of graphical user interface (GUI)screens that may be used to gather and/or present information regardingcrop status and alert users of a crop status or a change in crop statusin accordance with embodiments of the present invention. The GUIs shownin FIGS. 6-10 may be prepared by, for example, crop status analyzer andalert generator 110 and provided to a user, such as user 130 via aninterface, such as user interface 125.

FIGS. 6 and 7 illustrate two examples of alerts provided to the user.FIG. 6 shows an example of an alert message 600 that may be provided tothe user in response to a determined status of a crop. This exemplaryalert message includes a notification that a crops condition hasdeteriorated and that this deterioration may be to nitrogen stress. Thealert message further includes the location of the crop stress andadvises that he or she may want to personally investigate/inspect thelocation of the determined deterioration to make a final determination.The user may be the farmer or another person designated by the user toreceive the information, such as a crop consultant, buyer, supplier,landlord, or other designated person or organization.

In this example, new additional data was generated and received by thecrop status analyzer and alert generator during a data-gathering eventthat occurred at 10:23 am on Feb. 25, 2012. It is important to point outthat the information collected during the data gathering event wasquickly analyzed and the alert was generated within a timeframe thatallows the user to take timely corrective action and thereby minimizedamage to the crop that would have otherwise been caused by theconventional lag time between data gathering and data analysis and dataanalysis and alerting the user to and in potential crop stress. In thisexample, the data-gathering event was completed by a UAV that capturedthe data an hour before the alert was sent to the user. This additionalnew in-season data, combined with data already contained within database135, was then processed by the crop status analyzer and alert system todetermine a crop status score. In this example, the crop status scorefor 27% of the included acres triggered the crop status analyzer andalert generator to automatically generate an alert and send it to theappropriate user and/or those authorized by the user to receive thealert via communication network 105. The alerts may contain variouslevels of detail, such as the size and/or location of the area/gridswhere the analysis was performed. The alert may also contain contentthat is unique to a recipient, based on the preferences or roles of therecipient or preferences of the user.

FIG. 7 illustrates an exemplary GUI 700 that conveys informationrelating to the nutrient status of multiple fields to a user. GUI 700also illustrates changes in those statuses over time, and demonstratesthe use of an Internet web site as a user interface for an alert system,such as user interface 125. The number of fields and/or field sectionsdepicted in GUI 700 is user configurable. For example, GUI 700 conveysinformation relating to several (5) fields rather than just one so thatthe user can receive one alert for all the fields for which crop stresshas been determined rather than several alerts focusing on just onefield each. In addition, GUI 700 contains an indicator of crop stressseverity. A red stop sign icon is used to indicate a severe crop stress,while the yellow yield icon indicates a less severe crop stress. GUI 700also contains an indication of the crop status trend. These indicatorsmay help the user determine the next steps he or she wishes to take andthe level of urgency with which he or she decides to take them. Finally,GUI 700 contains information relating to forthcoming or planneddata-gathering events for the field.

FIG. 8 contains three images of a field captured by a UAV over a periodof days and illustrates changes in the crop's condition over time. Theseimages are indicative of the type of image and related data that thecrop status analyzer and alert generator will receive and analyze todetermine a field's nutrient status and changes in that status overtime. As previously described, the changes in a field's nutrient statusare measured, for example, based on texture, color (traditional andinfrared), patterns, tone, shadows, and temperature and are the basis ofgenerating a score of crop status by the crop status analyzer and alertgenerator. The present invention may employ computerized imagecomparisons to analyze images of a field and/or section of a field todiscover nuanced changes to the imaged area's status, determine cropstatus, and issue immediate, or nearly immediate, status alerts.

In some embodiments, certain visual and other display techniques may beincorporated into the display of an alert message such as GUI 700 inorder to make the changes in crop status that occur over time moreobvious. One such technique is amplification of visual indicators of thechange by electronic means via, for example, color or contrastadjustments that make the image easier to understand and illustrate thechange in a more dramatic manner. Another technique includesincorporation of time-lapse images that are sequenced on the screen oneafter another (like in a video) in a manner similar to display of asequence of time lapse weather radar images as commonly used today withweather radar images. When the user views the time-lapsed images certainpatterns within the data may become apparent. Exemplary patterns includetrending increases or decreases in the crop health of a field and ageographic direction in which crop health may be increasing ordecreasing.

FIG. 9 illustrates an example of a user interface 900 that may assist auser when reviewing information related to crop status. In thisembodiment, an alert has been triggered and the user has been notifiedof a potential problem on this field. However, it is understood that theuser can access this information regardless of whether or not an alerthas been triggered. It is also understood that the content displayed onuser interface 900 may vary depending on the role or preferences of theuser. In some embodiments, the presentation of the content on userinterface 900 may vary depending on the manner in which it is viewed(e.g., via smart phone, laptop computer, tablet computer, etc.).

User interface 900 provides a user with information regarding cropstatus of a specific field, or portion thereof, along with additionalinformation that may be helpful to the user. The layout of userinterface 900 includes field identifiers 910 as well as an image of thefield 920 being observed. Along with the field image 920, there is amodifiable field view window 930 that contains controls that allow theuser to alter the types of views of the field 920 displayed on userinterface 900. An analysis window 940 is also provided that displays thenutrient alert status, the areas of the field that are to be excludedfrom the analysis, and the triggers, or sensitivity parameters, which,if activated, result in an alert. The ability to take actions regardingthe issued alert 950 is also provided. Exemplary actions includeprinting user interface 900, emailing user interface 900, scheduling anevent associated with user interface 900, and attaching a note or otherdate to user interface 900. User interface 900 also includes a graphicalchart 960 that can show trends in, for example, nutrient status overtime, comparisons of nutrient status to benchmarks, and comparisons toother fields. Finally, chart view 970 enables the user to change thecontent or format of chart 960.

Field identifiers 910 may include, for example, the field and farm namesand locations, acreage, global positioning coordinates, latitude andlongitude coordinates, crop type, and/or ownership status.

In the example provided by user interface 900, field image 920 is theresult of a UAV and an in-season data gathering event. The sections intowhich the field is divided for the purpose of data gathering andanalysis is displayed in this embodiment. The various sizes and numberof sections may be user configurable according to one or more userdefined criteria, or determined based on, for example, one or morecriteria (e.g., level of precision required, specificity of informationdesired, equipment size), limitations of the imaging device, and/or theelevation at which the image was taken. While not shown on this samplescreen, it is understood that various types of information may beavailable to the user by moving a cursor over the image. For example,displaying consecutively multiple images taken over time enables theuser to view changes in crop status over time. In some embodiments, theuser may be able to zoom in on part of a field and thereby gain acloser, or more detailed, view of the area. In other embodiments, theviews available may depend on the capabilities of the user interfacedevice, on the capabilities of the UAV capturing the data, and/or on thetransmission capabilities of the communication network 105.

Modify field view window 930 enables the user to control the contentdisplayed on the field image 920. In this exemplary embodiment, the usercan overlay information relating to the crop and variety planted ontothe field image 920. This additional crop and variety information maychange the way the user views and interprets the data. This embodimentalso contains various ways to view the results of the analysis of thedata on the field image 920. For example, the user can request tovisually identify the portions of the field that have been determined tobe below defined benchmarks. For example, a user may elect, via modifyfield view window 930, to highlight or display only portions of thefield identified as in the bottom 10% (most severe crop stress) asdetermined by the crop status analyzer and alert generator. Likewise, auser may elect, via modify field view window 930, to view or highlightonly the portions of the field that have been improving and/or degradingover time. Each of these views will aid the user in makingdeterminations of what follow-up actions, if any, he or she may want totake in order to ameliorate the effects of any detected crop stress.Within field view area 930 the user is enabled to identify portions ofthe field that he or she wishes to exclude from the analysis (e.g.,locations of buildings or bodies of water). This capability enables auser to use his or her own local knowledge of the field and excludethose portions of the data that might naturally deviate from the datareceived for the intended area to be analyzed and throw off or distortthe data set. For example, the user may want to exclude former buildingsites from the analysis because they may skew the results. The cropstatus analyzer and alert generator uses this method to prevent needlessand unnecessary alerts from being sent to the user.

Analysis window 940 contains a summary of the nutrient status/alertanalysis, and identifies the triggers (e.g., deviation from a benchmarkor predefined threshold, a percentage change, or an amount of acreageimpacted by a crop status), which will cause an alert to be sent to theuser. In this example, the field crop status has been determined ashaving exceeded two of the parameters that trigger an alert. In thealerts portion of analysis window 940 the user is able to identify theparameters, which if exceeded, will trigger an alert to be sent to theuser via the communication network. In this example, the user hasidentified three parameters that he or she would like to be consideredwhen determining if an alert should be triggered. The first compares thefield to a predefined severity benchmark and when the severity exceedsthe predefined trigger the alert is activated. The second relates to thescope or number of acres and when the trigger exceeds this parameter analert is activated. Scope within a field is commonly measured as apercent of the field such as 4% of the field or specific number of acressuch as 30 acres. The third example illustrates a trigger where the userhas defined the field as a “watch field” where he or she wants toreceive updates whenever new data is received, regardless of thetriggers. This approach may be used for problematic fields where a userwants to pay special attention to the crop status. Of course, there maybe multiple ways for the crop status analyzer and alert generator tohandle these user-defined triggers. Triggers may relate to contractualobligations a buyer imposed or performance requirements from a landlord.Examples of different approaches include sending an alert to the userevery day, or each week, or every time additional data is acquired. Theuser is also able to add or delete triggers via interaction withanalysis window 940.

Capabilities to take actions based on the analysis are, in this example,indicated by the buttons 950 in the lower right portion. For example, itis possible to send an email to a supplier such that the supplier cansupply tools, goods, and/or services that may be used to correct thenutrient deficiency. Or the user may make a note, or schedule an event,or simply print a report. Of course, there are other actions that someembodiments may include that which would not detract from the intent ofthe present invention. In addition, specific information relating to thecrop status determination and potential follow-up actions may beincluded; for example, the coordinates of the portions of the field witha problem may be included in an email, as well as other information.

Graphical chart 960 allows the user to view additional types of data andanalysis results concerning status for this specific field or a portionthereof. In this embodiment, the data displayed on graphical chart 960is entirely user configurable. The depicted exemplary graphical chart960 includes an analysis of the nutrient scores and graphical depictionsof them in three base levels of severity. First, where N application isrecommended; second a gray area where the next steps are less obvious;and third status quo level where the nutrient level is satisfactory. Inthis example, the bottom of the chart consists of a timeline upon whichvarious data gathering events have occurred. In this example, a UAV hasgathered data in June. In some embodiments a comparison between the datafor this field and other fields and/or known benchmarks may be executed.The user may manually determine the benchmark by which he or she wantsthe actual data compared, or the benchmark could be determined based onhistorical data from this field, or possibly based on available onlineresearch by a university or other research organization concerning planthealth at specific times in its development cycle.

Finally chart view 970 enables the user to control the view of the datain the graphical chart 960 by enabling the user to select and controlwhat specific data is to be displayed on graphical chart 960. Forexample, the user may set up graphical chart 960 to display comparisonswith other fields he or she also farms and/or with those fields farmedby peer farmers. The comparison of this field to one farmed by a peerfarmer may be contingent on that peer farmer also using the presentinvention. This sharing of data, as mentioned before, could becommunicated via, for example, a social network, or anotherInternet-based communication method. The user may want to compare thispresent field to another in proximity to this field because these fieldsare most likely to be subject to the same weather and growingconditions. In some embodiments, the user may be limited to comparisonsto fields that have specific characteristics, such as planting date,varieties, soil types, farming practices and the like.

FIG. 10 illustrates an exemplary screen 1000 further detailing the typeof information determined and provided by the crop status analyzer andalert generator. In this example, the content and controls are the sameas those described with regard to FIG. 9 except that the exclusion layerdata has been turned off and these areas are now included in theanalysis.

1. A method comprising: receiving, by a computer from a plurality ofdata sources, data associated with at least a portion of a field ofplants, the data received from each of the plurality of data sourcescorresponding to a different one of a plurality of categories, theplurality of categories including at least a crop stress category, anadded fertility category, and a soils category, each of the plurality ofcategories associated with one or more category-specific sub-categoriescorresponding to data received from at least one of the plurality ofdata sources; determining, by the computer, an overall score for thereceived data that indicates a nutrient status of at least the portionof the field of plants, wherein determining the overall score comprises:determining a sub-category score for each of the one or moresub-categories; applying a weighting factor to each sub-category scoreto determine a plurality of weighted sub-category scores; determining asub-score for each category by aggregating the plurality of weightedsub-category scores corresponding to the respective category, eachsub-score corresponding to a category-specific nutrient status of atleast the portion of the field of plants; applying a weighting factor toeach of the sub-scores to determine a plurality of weighted sub-scores;and aggregating the plurality of weighted sub-scores to determine theoverall score; and outputting, by the computer, an alert to at least oneof a user and a designated party in response to determining that theoverall score satisfies a threshold value.
 2. The method of claim 1,wherein the alert is provided within a time period that enables at leastone of the user and the designated party to take corrective action toimprove the nutrient status of at least the portion of the field ofplants.
 3. The method of claim 1, wherein the designated party is one ofan agricultural product supplier, an agricultural service provider, anagricultural product buyer, and a landlord.
 4. The method of claim 1,further comprising: storing the alert in a database.
 5. The method ofclaim 1, wherein the alert indicates a level of distress to plantswithin at least the portion of the field of plants.
 6. The method ofclaim 1, wherein the alert indicates a location of distressed plantswithin at least the portion of the field of plants.
 7. The method ofclaim 1, further comprising: determining a recommendation responsivelyto determining that the overall score satisfies the threshold value, therecommendation providing one or more actions that may be taken toaddress subject matter of the alert.
 8. The method of claim 1, furthercomprising: tailoring the alert according to at least one of a usercharacteristic, a user preference, a type of alert, an amount by whichthe overall score exceeds the threshold value, and a type of the overallscore.
 9. The method of claim 1, wherein the alert includes at least oneof the overall score, a visual representation of a location of plantsthat do not satisfy the threshold value, a visual representation of atrend of a status of the field of plants over a period of time, a chartshowing one or more trends of a status of the field of plants, a visualdisplay of locations of the field of plants excluded from statusdeterminations, and a visually enhanced display of one or more portionsof the received data as selected by a user.
 10. The method of claim 1,wherein the alert includes received data for at least the portion of thefield of plants received at different times.
 11. The method of claim 1,wherein at least one of receiving the data associated with at least theportion of the field of plants and outputting the alert is executed viaa social networking service.
 12. The method of claim 1, furthercomprising: receiving, by the computer, an indication of one or moreuser-excluded geographic areas within at least the portion of the fieldof plants; wherein determining the overall score for the received datacomprises excluding data corresponding to the one or more user-excludedgeographic areas.
 13. The method of claim 12, further comprising:updating at least one of the received data and the overall score inresponse to receiving the indication of the one or more user-excludedgeographic areas.
 14. The method of claim 1, further comprising:determining a recommendation responsively to determining that theoverall score satisfies the threshold value, the recommendationproviding one or more actions that may be taken to address subjectmatter of the alert.
 15. The method of claim 14, wherein therecommendation providing the one or more actions is proportionate to anamount that the overall score deviates from the threshold value.
 16. Themethod of claim 1, wherein the crop stress category is associated withat least a near-infrared (NIR) imagery sub-category.
 17. The method ofclaim 1, wherein the added fertility category is associated with atleast a frequency of nitrogen application sub-category.
 18. The methodof claim 1, wherein the soils category is associated with at least asoil texture category.
 19. A system comprising: at least one computer;an image sensor; and a crop status analyzer and alert generatorexecutable by the at least one computer and configured to: receive, froma plurality of data sources that includes the image sensor, data for atleast a portion of a field of crops, the data received from each of theplurality of data sources corresponding to one of a plurality ofcategories regarding a status of at least the portion of the field ofcrops, the plurality of categories including at least a crop stresscategory, an added fertility category, and a soils category, each of theplurality of categories associated with one or more_sub-categories;determine, based on the received data, an overall score corresponding toa nutrient status of at least the portion of the field of crops by atleast determining a sub-category score for each of the one or moresub-categories, applying a weighting factor to each sub-category scoreto determine a plurality of weighted sub-category scores, aggregatingthe plurality of weighted sub-category scores corresponding to therespective category to determine a sub-score for each category, applyinga weighting factor to each of the sub-scores to determine a plurality ofweighted sub-scores, and aggregating the plurality of weightedsub-scores to determine the overall score; determine that the overallscore is not included within a range of acceptable overall scores; andoutput an alert to at least one of a user and a designated party inresponse to determining that the overall score is not included withinthe range of acceptable overall scores.
 20. A method comprising:receiving, by a computer from a plurality of data sources, datacorresponding to crops, the data received from each of the plurality ofdata sources corresponding to a different one of a plurality ofcategories, the plurality of categories including at least a crop stresscategory, an added fertility category, and a soils category, each of theplurality of categories associated with one or more sub-categories;determining, by the computer, a category score for each category basedon an aggregation of weighted sub-category scores corresponding to eachsub-category associated with a respective category; applying, by thecomputer, a weight to each category score; aggregating, by the computer,the weighted category scores to determine a crop status score; andoutputting, by the computer, an alert in response to determining thatthe crop status score deviates from a threshold value, the alertincluding a recommendation for corrective action to be taken thatcorresponds to an amount that the overall score deviates from thethreshold value.